Abstract
Dear Colleagues:
This year we recognize the 40-year anniversary of the historic and far-implicating Cohen-Boyer genetic experiments that led to engineering DNA, aka recombinant DNA (rDNA) technology. An entire industry blossomed from these discoveries, starting from Genentech, Cetus, and Chiron, to the ultimate utilization and proliferation of these rDNA tools in large and small pharmaceutical companies. But what is in a name?
Biotechnology has been used for millennia to make useful products for mankind, eg, beginning with the universally loved cheese, beer, and wine, as noted in Homer's epic Greek poems from 800 BC. Prior to modern biotechnology (pre-1970s), strain improvement was carried out by more classical methods, eg, chemical and/or radiation mutagenic methods or natural mutagenesis via selective pressure. Still, today, these classical methods are utilized for strain improvement to unblock certain unknown restrictions met after much molecular methods implementation. Further exquisite refinements of this technology became possible in the 1970s, and the Cohen-Boyer experiments led the way. Over the past 40 years, more than 4,400 genomes have been completely sequenced and hundreds of production strains have been developed using the many tools of rDNA technology. Modern strain development and the manipulation of DNA may often be referred to as biotechnology, genetic engineering, molecular cloning, recombinant DNA, synthetic biology, and more recently, systems biology, etc. From Wikipedia we can extract: • Biotechnology is the use of living systems and organisms to develop, modify or make useful products or processes. • Genetic engineering is the direct manipulation of an organism's genome using biotechnology. • Molecular cloning is a set of experimental methods in molecular biology that are used to assemble recombinant DNA molecules and to direct their replication within host organisms. • Recombinant DNA (rDNA) molecules are DNA sequences that result from the use of laboratory methods (molecular cloning) to bring together genetic material from multiple sources, creating sequences that would not otherwise be found in biological organisms. • Synthetic biology is the design and construction of biological devices and systems for useful purposes. It encompasses a variety of different approaches, methodologies, and disciplines with a focus on engineering biology and biotechnology. • Systems biology is an emerging approach applied to biomedical and biological scientific research. Systems biology is a biology-based inter-disciplinary field of study that focuses on complex interactions within biological systems, using a more holistic perspective and approach to biological and biomedical research.
Then we even have color-coded biotechnology: • Blue biotechnology encompasses the marine and aquatic applications of biotechnology. • Green biotechnology is biotechnology applied to agricultural processes. • Red biotechnology is applied to medical processes. • White biotechnology, also known as industrial biotechnology, is biotechnology applied to industrial processes.
Call it what you will, in any color, the utilization of all of these biotechnological tools and systems has led to tremendous developments in strain productivity leading to many hundreds of useful products in both the industrial and pharmaceutical fields.
Systems biology has only recently entered the popular biotechnology lexicon, but perhaps it is systems biology that brings it all together. The coupling of all of the “omics” (genomics, transcriptomics, epigenomics, proteomics, glycomics, lipidomics, metabolomics, fluxomics, and likely some omics I left out!) combined with iterations involving bioinformatics and hypothesis-driven in silico modeling and simulation, has led to enormous productivity and yield improvements, as well as strain and process developments. However, there is still a lot to be done, with the ultimate challenge being to link, in a reliable manner, a microorganism's genotype and phenotype and to predict the overall system's outcome from biotechnological perturbations.
Any manipulation of a cell factory for the ultimate goal of increased productivity ideally requires the integrated knowledge and coordination of multiple cellular processes. This issue of IB includes several papers that use systems biology. They take a holistic view and approach for overall strain development, both prokaryotic and eukaryotic, leading to improvements in strain productivity.
We start with a conversation with Jay Keasling, PhD, recipient of the 2013 George Washington Carver Award for Innovation in Industrial Biotechnology. Dr. Keasling has been an outspoken proponent and active user of systems biology approaches for product and strain development. He provides some thoughts on needs in the field and where industrial biotechnology may be headed.
Garcia-Albornoz and Nielsen provide an insightful discussion and application of genome-scale metabolic models, which together with developed in silico methods can be used to simulate and model metabolic pathways in order to predict systems behavior under various environmental conditions, leading to bioprocesses for producing biofuels, biomaterials, food ingredients, and pharmaceuticals. Bazilian et al. describe the use of algal bioresources to evaluate the nexus of energy-water-food. They discuss the use of algae and their tradeoffs to meet opportunities for increased energy resources, enhanced food supplies, greenhouse gas mitigation, and wastewater remediation.
Algae have been useful for producing many important products—eg, omega-3 fatty acids—but their use in biofuels production has been met with many challenges. Koskimaki et al. review the existing computational models and databases of algal metabolism, with an eye to improving algae-to-energy production. Utilizing genome-scale kinetic models of metabolism, Smallbone and Mendes define a pipeline of such data that can be used, for example, to expand a detailed model of yeast glycolysis to the genome level. Markus and Harrison summarize the benefits and challenges in using broader measurements of cellular metabolic states in cells vs. targeted metabolic profiling methods for engineering of more productive cell factories. Hyland et al. use genome metabolic models to analyze critical economic trade-offs between acid neutralization and productivity yields (ethanol) in lignocellulosic-based yeast fermentation processes.
The recent Supreme Court ruling that human genes cannot be patented (the Myriad Genetics case) has caused a bit of a stir. In this issue, Fraser and Rosen provide some insights into the post-Myriad patent landscape scene. Patent Updates also provide a review of biofuels patent publications for the first half of 2013.
To date more than 4,400 genomes have been sequenced and either partially or completely annotated. It is a common misconception that these genomes, sequences, and/or annotations are all correct. Quite often these annotated genomes contain significant errors in genes and their encoded proteins, as well as the pathway biomolecular interactions. These inaccuracies lead to dramatic problems when used in systems biology for increasing cellular productivity. Aung et al. have analyzed and amended important inconsistencies in the Yeast v6.0 model relating to fatty acid, glycerolipid, and glycerophospholipid metabolism. Modeling and altering these pathways will be critical for metabolically engineering high lipid productivity in cells to achieve commercial production of economically favorable and environmentally sustainable biodiesel fuels.
King and Feist, using a novel computational method—OptSwap—to identify optimal modifications of cofactor binding specificities of oxidoreductases and complementary reaction gene knockouts, have been able to optimize product yields of substrate specific production in E. coli. They discuss broader utilization of the techniques.
Just 10 years ago it took more than 5 years to go from a gene sequence to a scaled, commercialized product. Today it can be done in less than 1 year. DNA sequencing has made dramatic advances in speed and lowered costs over the past decade. Every year the large industrial biotechnology companies reduce their tremendous need for capital expansion by applying the advances of rDNA, biotechnology, molecular cloning, genetic engineering, synthetic biology, and now systems biology tools.
Understanding the biomolecular landscape that describes cellular phenotype is the ultimate goal of much of science and certainly of strain developers. Having the requisite tools on hand to make the cellular complexity understandable, manageable, integratable, and manipulatable is of key importance. In many ways, it is the only chance for clear scientific advances in strain improvement, commercial profitability, and survival. My colleagues and I hope that this issue of Industrial Biotechnology will provide you with further insights into this fascinating field of biotechnology and systems biology.